diff --git a/ray_data_robotics_droid_blip/README.md b/ray_data_robotics_droid_blip/README.md new file mode 100644 index 0000000..a8548c3 --- /dev/null +++ b/ray_data_robotics_droid_blip/README.md @@ -0,0 +1,52 @@ +# DROID → BLIP Captioning Pipeline + +A [Ray Data](https://docs.ray.io/en/latest/data/overview.html) pipeline that reads the [DROID Raw 1.0.1](https://droid-dataset.github.io/) robotics dataset from a public S3 bucket, generates image captions for each wrist-camera frame using [BLIP-large](https://huggingface.co/Salesforce/blip-image-captioning-large) on GPU, and writes annotated parquet partitioned by episode. + +Demonstrates **heterogeneous Ray Data pipelines** — CPU workers stream data from S3 while GPU workers run model inference, with Ray Data managing backpressure automatically. + +## Files + +| File | Description | +|---|---| +| `pipeline.py` | Main entry point — pipeline wiring, CPU/GPU stages, and config knobs | +| `job.yaml` | Anyscale job config — compute, env vars, T4 GPUs | +| `pyproject.toml` | Dependencies (managed by `uv`) | + +**Note:** The pipeline automatically downloads the DROID episode manifest (`episodes_droid_v1.0.1_s3.parquet`) from S3 on first run. + +## Usage + +```bash +# Install dependencies +uv sync + +# Run on an existing Ray cluster +uv run python pipeline.py + +# Smoke test (limit to N episodes) +# Set NUM_EPISODES in pipeline.py, then run as above +``` + +### Run on Anyscale + +Submit as a job using the included `job.yaml`, which provisions T4 GPU workers: + +```bash +uv run anyscale job submit -f job.yaml + +# Override GPU concurrency or batch size via env vars +uv run anyscale job submit -f job.yaml --env GPU_STAGE_CONCURRENCY=8 --env GPU_BATCH_SIZE=64 +``` + +## Configuration + +Key settings in `pipeline.py`: + +| Setting | Default | Description | +|---|---|---| +| `NUM_EPISODES` | `1000` | Limit episodes for testing (set to `None` for all) | +| `MODEL_NAME` | `Salesforce/blip-image-captioning-large` | HuggingFace model ID | +| `CPU_LOADER_NUM_CPUS` | `0.5` | Fractional CPUs per data-loading worker | +| `GPU_BATCH_SIZE` | `32` | Frames per GPU batch (env: `GPU_BATCH_SIZE`) | +| `GPU_CONCURRENCY` | `2` | Number of GPU actors (env: `GPU_STAGE_CONCURRENCY`) | +| `OUTPUT` | `/mnt/cluster_storage/droid-annotated` | Parquet output path | diff --git a/ray_data_robotics_droid_blip/job.yaml b/ray_data_robotics_droid_blip/job.yaml new file mode 100644 index 0000000..b6779e5 --- /dev/null +++ b/ray_data_robotics_droid_blip/job.yaml @@ -0,0 +1,49 @@ +# View the docs https://docs.anyscale.com/reference/job-api#jobconfig. +name: droid-blip-captioning + +# When empty, use the default image. This can be an Anyscale-provided base image +# like anyscale/ray:2.43.0-slim-py312-cu125, a user-provided base image (provided +# that it meets certain specs), or you can build new images using the Anyscale +# image builder at https://console.anyscale-staging.com/v2/container-images. +image_uri: anyscale/ray:2.54.0-slim-py313-cu129 + +compute_config: + head_node: + required_resources: + CPU: 8 + memory: 32Gi + worker_nodes: + # CPU nodes for I/O-bound episode loading (S3 download + video decode). + - required_resources: + CPU: 8 + memory: 32Gi + min_nodes: 8 + max_nodes: 8 + # GPU nodes for BLIP inference (T4). + - required_resources: + CPU: 4 + memory: 16Gi + GPU: 1 + required_labels: + ray.io/accelerator-type: T4 + min_nodes: 4 + max_nodes: 4 + +# Path to a local directory or a remote URI to a .zip file (S3, GS, HTTP) that +# will be the working directory for the job. The files in the directory will be +# automatically uploaded to the job environment in Anyscale. +working_dir: . + +env_vars: + GPU_STAGE_CONCURRENCY: "4" + GPU_BATCH_SIZE: "32" + +# The script to run in your job. You can also do "uv run main.py" if you have a +# pyproject.toml file in your working_dir. +entrypoint: uv run python pipeline.py + +# If there is an error, do not retry. +max_retries: 0 + +# Kill the job after 4 hours to control costs. +timeout_s: 14400 diff --git a/ray_data_robotics_droid_blip/pipeline.py b/ray_data_robotics_droid_blip/pipeline.py new file mode 100644 index 0000000..d01e224 --- /dev/null +++ b/ray_data_robotics_droid_blip/pipeline.py @@ -0,0 +1,309 @@ +""" +DROID → BLIP Captioning Pipeline (Ray Data) +============================================ + +This script demonstrates a **heterogeneous Ray Data pipeline** that reads +video files from a public S3 bucket, decodes the first frame of each, +generates an image caption using a vision-language model on GPU, and writes +the results to Parquet. + +The dataset is DROID Raw 1.0.1 — a large-scale robotics manipulation +dataset with ~59K episodes across 13 research labs. + +Key Ray Data concepts demonstrated +----------------------------------- +1. **read_binary_files** — parallel I/O from S3, one file per task. +2. **map (stateless)** — lightweight per-row transforms on CPU workers. +3. **map_batches (stateful, GPU)** — actor-pool pattern for model inference. + The model loads once per actor in __init__; __call__ runs per batch. +4. **Streaming execution** — all stages run concurrently with automatic + backpressure. No stage waits for the previous one to finish. + +Pipeline +-------- + ┌─────────────────────────────────────────────────────┐ + │ read_binary_files(mp4_paths) [CPU, I/O] │ + │ Download each MP4 from S3 in parallel. │ + ├─────────────────────────────────────────────────────┤ + │ map(attach_meta) [CPU] │ + │ Look up episode uuid + task from manifest. │ + ├─────────────────────────────────────────────────────┤ + │ map(decode_first_frame) [CPU] │ + │ Decode the first video frame with PyAV. │ + ├─────────────────────────────────────────────────────┤ + │ map_batches(BlipCaptionStage) [GPU] │ + │ BLIP-large fp16 captioning, batched across GPUs. │ + ├─────────────────────────────────────────────────────┤ + │ drop_columns(["frame"]) │ + │ Remove the raw image; keep uuid, task, caption. │ + ├─────────────────────────────────────────────────────┤ + │ write_parquet(OUTPUT) │ + │ Materialize the full pipeline and write results. │ + └─────────────────────────────────────────────────────┘ + +Usage +----- + # Smoke test with 100 episodes on 4 GPUs: + GPU_STAGE_CONCURRENCY=4 uv run python pipeline.py + + # Full dataset: + NUM_EPISODES=None (edit below) +""" + +from __future__ import annotations + +import io +import logging +import os +import time +from typing import Any + +import av +import numpy as np +import pyarrow.parquet as pq +import ray +import ray.data +import torch +from PIL import Image +from pyarrow.fs import S3FileSystem + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)-8s %(message)s") +log = logging.getLogger(__name__) + + +# ============================================================================ +# Configuration +# ============================================================================ + +# -- Dataset -- +MANIFEST = "episodes_droid_v1.0.1_s3.parquet" +DATASET_BUCKET = "s3://anyscale-public-droid-dataset" +DATASET_PREFIX = "droid/1.0.1" + +# -- Output -- +OUTPUT = "/mnt/cluster_storage/droid-annotated" + +# -- Limits -- +# Set to an integer to cap the number of episodes (useful for smoke tests). +# Set to None to process the full dataset. +NUM_EPISODES: int | None = None + +# -- Model -- +MODEL_NAME: str = "Salesforce/blip-image-captioning-large" + +# -- Runtime environment -- +# Example: propagate a custom env var to all Ray workers. +# See: https://docs.ray.io/en/latest/ray-core/handling-dependencies.html +CUSTOM_ENV_VAR: str = "hello" + +# -- CPU stage resources -- +# Each CPU map worker needs little CPU since the work is I/O-bound +# (downloading from S3 + video decoding). Setting < 1 lets Ray schedule +# more workers per node, increasing download parallelism. +# See: https://docs.ray.io/en/latest/data/transforming-data.html#configuring-parallelism +CPU_LOADER_NUM_CPUS: float = 0.5 + +# -- GPU stage resources -- +# Number of BLIP actors. Each actor claims one GPU and processes batches +# of images. Override at runtime: GPU_STAGE_CONCURRENCY=8 uv run ... +# See: https://docs.ray.io/en/latest/data/transforming-data.html#stateful-transforms +GPU_CONCURRENCY: int = int(os.environ.get("GPU_STAGE_CONCURRENCY", "2")) +GPU_WORKER_NUM_GPUS: float = 1.0 + +# Rows per GPU batch. Increase until VRAM is ~80% utilized. +# BLIP-large fp16 uses ~1.5 GB model weight; batch of 32 images fits +# comfortably on most GPUs. +GPU_BATCH_SIZE: int = int(os.environ.get("GPU_BATCH_SIZE", "32")) + + +# ============================================================================ +# Stage 1 (CPU): Decode first video frame +# ============================================================================ +# This is a *stateless* map function — Ray Data calls it once per row. +# It receives the raw MP4 bytes downloaded by read_binary_files and +# returns a single decoded RGB frame (HWC uint8 numpy array). + + +def decode_first_frame(row: dict[str, Any]) -> dict[str, Any]: + """Decode the first RGB frame from in-memory MP4 bytes.""" + buf = io.BytesIO(row["bytes"]) + frame = None + with av.open(buf, mode="r") as container: + for f in container.decode(video=0): + frame = f.to_ndarray(format="rgb24") + break + row["frame"] = frame + del row["bytes"] + return row + + +# ============================================================================ +# Stage 2 (GPU): BLIP image captioning +# ============================================================================ +# This is a *stateful* map class — Ray Data creates a pool of actors +# (one per GPU). __init__ runs once to load the model; __call__ is +# invoked on each batch. This avoids reloading the model per batch. +# +# See: https://docs.ray.io/en/latest/data/transforming-data.html#stateful-setup-with-actors + + +class BlipCaptionStage: + """BLIP-large image captioning actor for Ray Data map_batches.""" + + def __init__(self, model_name: str = MODEL_NAME) -> None: + from transformers import BlipForConditionalGeneration, BlipProcessor + + log.info("Loading BLIP model: %s …", model_name) + t0 = time.monotonic() + + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.processor = BlipProcessor.from_pretrained(model_name) + self.model = ( + BlipForConditionalGeneration.from_pretrained( + model_name, torch_dtype=torch.float16 + ) + .to(self.device) + .eval() + ) + + log.info( + "BLIP loaded on %s in %.1fs (fp16, %d params).", + self.device, + time.monotonic() - t0, + sum(p.numel() for p in self.model.parameters()), + ) + + def __call__(self, batch: dict[str, Any]) -> dict[str, Any]: + """Caption a batch of images. + + Ray Data calls this with a dict of column → list[value], + not one row at a time. This enables efficient batched GPU inference. + """ + images_raw: list[np.ndarray] = batch["frame"] + n = len(images_raw) + t0 = time.monotonic() + + pil_images = [Image.fromarray(arr, mode="RGB") for arr in images_raw] + + with torch.inference_mode(): + inputs = self.processor( + images=pil_images, return_tensors="pt", padding=True, + ).to(self.device, torch.float16) + generated_ids = self.model.generate(**inputs, max_new_tokens=128) + + batch["caption"] = self.processor.batch_decode( + generated_ids, skip_special_tokens=True + ) + + elapsed = time.monotonic() - t0 + log.info( + "GPU batch: %d frames in %.2fs (%.1f frames/s)", + n, elapsed, n / elapsed if elapsed > 0 else 0.0, + ) + return batch + + +# ============================================================================ +# Prepare: read manifest and resolve S3 paths +# ============================================================================ +# The manifest is a small Parquet file (~85K rows) listing every episode. +# We read it directly from S3 on the driver to build: +# 1. A list of MP4 paths to pass to read_binary_files. +# 2. A path → {uuid, task} lookup so we can attach metadata later. + +# Anonymous S3 filesystem for the public DROID bucket. +s3_fs = S3FileSystem(anonymous=True, region="us-east-2") + +# Construct S3 path (remove s3:// prefix for PyArrow filesystem) +bucket_name = DATASET_BUCKET.replace("s3://", "") +manifest_path = f"{bucket_name}/{DATASET_PREFIX}/{MANIFEST}" +log.info("Reading manifest from S3: s3://%s", manifest_path) +manifest = pq.read_table(manifest_path, filesystem=s3_fs) +s3_base = f"{DATASET_BUCKET}/{DATASET_PREFIX}/" + +mp4_paths: list[str] = [] +path_to_meta: dict[str, dict[str, str]] = {} + +for i in range(len(manifest)): + rel = manifest.column("ext1_mp4_path")[i].as_py() + if not rel: + continue + full = rel if "://" in rel else s3_base + rel + mp4_paths.append(full) + path_to_meta[full] = { + "uuid": manifest.column("uuid")[i].as_py(), + "task": manifest.column("task")[i].as_py(), + } + +if NUM_EPISODES is not None: + mp4_paths = mp4_paths[:NUM_EPISODES] + +log.info("Processing %d episodes (of %d total).", len(mp4_paths), len(manifest)) + + +# ============================================================================ +# Connect to the Ray cluster +# ============================================================================ +# ray.init connects to an existing cluster (started by Anyscale or `ray start`). +# runtime_env propagates environment variables to every worker. + +ray.init( + address="auto", + ignore_reinit_error=True, + runtime_env={"env_vars": {"CUSTOM_ENV_VAR": CUSTOM_ENV_VAR}}, +) + +# Put the metadata lookup in the Ray object store so all workers can +# access it without serializing it per task. +meta_ref = ray.put(path_to_meta) + + +def attach_meta(row: dict[str, Any]) -> dict[str, Any]: + """Look up episode uuid and task from the manifest by file path.""" + meta = ray.get(meta_ref) + info = meta.get(row["path"], {}) + row["uuid"] = info.get("uuid", "") + row["task"] = info.get("task", "") + del row["path"] + return row + + +# ============================================================================ +# Build and run the pipeline +# ============================================================================ +# Everything below is lazy — no work happens until write_parquet() is called. +# Ray Data then executes all stages as a streaming pipeline with automatic +# backpressure between CPU and GPU stages. + +ds = ( + # --- Read: download MP4 files from S3 in parallel --- + # Each file becomes one row with columns: bytes, path. + # See: https://docs.ray.io/en/latest/data/loading-data.html + ray.data.read_binary_files( + mp4_paths, + filesystem=s3_fs, + include_paths=True, + ignore_missing_paths=True, + ) + # --- CPU: attach episode metadata from manifest --- + .map(attach_meta) + # --- CPU: decode first video frame from MP4 bytes --- + .map(decode_first_frame, num_cpus=CPU_LOADER_NUM_CPUS) + # --- GPU: generate captions with BLIP-large --- + # ActorPoolStrategy creates a fixed pool of GPU actors. + # See: https://docs.ray.io/en/latest/data/transforming-data.html#computing-over-batches + .map_batches( + BlipCaptionStage, + batch_size=GPU_BATCH_SIZE, + num_gpus=GPU_WORKER_NUM_GPUS, + concurrency=GPU_CONCURRENCY, + ) + # --- Drop the raw image; keep only uuid, task, caption --- + .drop_columns(["frame"]) +) + +# write_parquet triggers execution of the full pipeline. +# Everything above is lazy — this call materializes the DAG. +ds.write_parquet(OUTPUT) + +log.info("Done. Output written to %s", OUTPUT) diff --git a/ray_data_robotics_droid_blip/pyproject.toml b/ray_data_robotics_droid_blip/pyproject.toml new file mode 100644 index 0000000..80d5225 --- /dev/null +++ b/ray_data_robotics_droid_blip/pyproject.toml @@ -0,0 +1,46 @@ +[project] +name = "ray-droid-blip-demo" +version = "0.1.0" +description = "Ray Data pipeline: DROID robotics dataset → BLIP image captioning" +readme = "README.md" +requires-python = ">=3.13, <3.14" +dependencies = [ + # Ray — data processing + cluster management + "ray[data,default]", + # Vision-language model and tokenizer + "transformers>=4.40,<5.0", + # PyTorch — GPU inference; install CUDA-enabled wheel separately on workers + "torch", + "torchvision", + # Video decoding (pure C, no ffmpeg subprocess) + "av", + # S3 access — streaming download without touching disk + "boto3", + "smart-open[s3]", + # Data formats + "pyarrow", + "h5py", + # Image encoding / resize + "pillow>=11.1.0", + "numpy", + # Anyscale — cluster and job 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